With the emergence of new communication technologies, multimedia services, such as e.g. Multimedia streaming, are becoming more popular to a large extent due to improved coverage and quality for media content, such as e.g. video, that may be distributed to various types of user devices, including both stationary, as well as mobile, user devices.
To ensure good quality of experience for such a media service, telecommunication system operators for both wireless and wired networks normally use tools and measurements to locate and prevent problems of a network or service, already close to the source.
With a tool that is adapted to recognize significant characteristics of the media content, the result of such an analysis can also be used for optimizing the network, thereby enabling more users to have a user experience of an adequate quality for the services offered, given certain network resources that are available to a service provider.
Some services only require that a few network parameters, such as e.g. the throughput, be measured to give a good estimate of the quality for the end user. When it comes to multimedia services, such as e.g. video streaming, the task of measuring quality of experience is not just as trivial since there are several factors that may contribute to a degradation of the quality, including the media itself.
Video quality assessment is related to the human perception of video and can be divided into subjective and objective methods. While subjective test methods normally give the best results, they are also expensive and time consuming, as a number of test persons have to be used to grade each video sequence, before a Mean Opinion Score (MOS) can be calculated, if a reliable result is to be achieved. Objective models, which can be used for real-time quality assessment, are often designed to output a score that estimates subjective MOS.
Objective models can be divided into three categories, namely perceptual models, parametric models and bit-stream models.
Perceptual models often depend on a number of image analysis algorithms to emphasize and quantify artifacts in the video. Perceptual models are also often divided into full-reference (FR) models, reduced-reference (RR) models and no-reference (NR) models. Perceptual NR models only use a decoded video as input to the model, while FR models also use the associated, uncoded reference as processing input. Instead of using the full reference, RR models use information about the reference as input via a narrow side-channel.
A number of standards exist for perceptual models such as e.g. the ITU-T Rec. J-144, which refers to different FRTV Multimedia (MM) models, ITU-T Rec. J-246, which refers to RRMM models, while ITU-T Rec. J-247 refers to FRMM models. The performance of the NR perceptual models is however still too poor to have any models standardized.
Parametric models known from prior art, commonly use different network parameters and codec information as input to estimate a bit stream quality score. Although the performance of parametric models tend to be worse than that of FR perceptual models, parametric models generally have a low grade of complexity and do not need the computational power that is normally required by perceptual models. Parametric models can therefore easily be implemented both in network nodes, and in user equipment, such as e.g. set-top boxes (STBs) and mobile devices.
Video bit stream models use a compressed video bit stream as input when the quality of the bit stream is to be estimated. Bit stream models are somewhat more complex than parametric models since more data needs to be parsed. However, since information about the coding can be acquired, these types of models have the potential of providing an estimation of the video quality with better accuracy than what can be achieved with alternative models.
There are many new encoders available on the market today, such as e.g. Handbreak, Quicktime, and Helix. When bit streams from these encoders are analyzed it is observed that the setups of encoder features may differ, even if the same profile and level is used. The amount of encoder features enabled affects the computational power needed for the encoder, and as a consequence, the time it takes to encode a frame may differ from one setup to another. This also affects the amount of computational power needed on the decoder side and, thus, the encoder can chose to limit the amount of feature if it is known, or likely that there is some type of resource limitations, such as e.g. computational power limitations or memory limitations, on the decoder side.
By using different setups of coding features at the encoder, these setups may be seen as corresponding to different, respective complexity levels, where e.g. the splitting of macro blocks into smaller partitions, and resolutions of the motion vectors during motion estimation are typical features that will affect the computational power.
This type of features is normally used to decrease the residual in the encoder loop, as well as to create an opportunity to increase the quality of the encoded stream.
The U.S. Pat. No. 6,011,868 describes a method for analyzing the quality of a video bit stream by using information about quantization and DCT coefficients.
It is also commonly known to use transport and video stream parameters to calculate a bit stream quality score, as well as to base the calculations on data, such as e.g. motion vector information and intra/inter-predicted frame/slice ratios, i.e. information that relates to the actual bit stream, given a certain encoding.
Conference paper “Proposal of a new QoE assessment approach for quality management of IPTV services”, ICIP 2008, presents an IPTV bit stream model that rely only on a quantization parameter (QP) when a bit stream quality score is calculated.
A common problem with the prior art bit stream quality model solutions are that although the complexity of the encoding procedure may differ from one case to another, this will not reflect the processing of the bit stream quality model selected for obtaining a bit stream quality score. Consequently, a bit stream quality model that is optimized for a specific encoder complexity will be used with the same data settings for different scenarios, involving encoders using different grades of complexity.
As a consequence from this, the accuracy of a resulting bit stream quality score may vary between measures, even though the same data settings are used as input for the used bit stream quality model.
Therefore, there is a need for a method and arrangement that enables an improved bit stream quality assessment.